The use of cardiovascular magnetic resonance for the assessment of left ventricular hypertrophy

Clinical characteristics of patients with high extracellular volume fraction evaluated by cardiac computed tomography for coronary artery evaluation

Aims

This study aims to evaluate the distribution of extracellular volume fraction detected via computed tomography, clinical characteristics of high extracellular volume fraction detected via computed tomography, and the rate of incidental detection of cardiac amyloidosis in patients undergoing cardiac computed tomography for coronary artery evaluation.

Methods and results

This study included 874 consecutive patients (mean age, 74.4 ± 7.1 years; men, 65%), comprising men aged ≥60 years and women aged ≥70 years, who had undergone cardiac computed tomography between January 2020 and September 2022. The mean extracellular volume fraction detected via computed tomography was 29.7 ± 5.2%, and 108 patients (12.4%) had an extracellular volume fraction detected via computed tomography of ≥35%. Older age (75.9 ± 8.2 years vs. 74.2 ± 6.9 years; P = 0.042), male sex (75.9% vs. 63.0%; P = 0.007), impaired left ventricular ejection fraction, increased high-sensitivity cardiac troponin T and B-type natriuretic peptide levels, and increased left ventricular thickness showed significant associations with an extracellular volume fraction detected via computed tomography of ≥35%. Cardiac amyloidosis was diagnosed incidentally in 15 patients based on an increase in extracellular volume fraction detected via computed tomography. The prevalence of cardiac amyloidosis was 1.7% (15/874) and 14.3% (15/105) in the entire study population and in patients with an extracellular volume fraction detected via computed tomography of ≥35%, respectively. An increase in the extracellular volume fraction detected via computed tomography was suggestive of cardiac amyloidosis.

Conclusion

Elevated extracellular volume fraction detected via computed tomography, associated with elevated cardiac biomarker levels and myocardial structural changes, may lead to the incidental diagnosis of cardiac amyloidosis.

The Role of Cardiovascular Magnetic Resonance Imaging in Patients with Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and poor quality of life. Cardiovascular magnetic resonance (CMR) imaging, with its unsurpassed capability of non-invasive tissue characterisation, high accuracy, and reproducibility of measurements, plays an integral role in determining the underlying aetiology of cardiac arrhytmias. CMR can reliably diagnose previous myocardial infarction, non-ischemic cardiomyopathy, characterise congenital heart disease and valvular pathologies, and also detect the underlying substrate concealed on conventional investigations in a significant proportion of patients with arrhythmias. Determining the underlying substrate of arrhythmia is of paramount importance for treatment planning and prognosis. However, CMR imaging in patients with irregular heart rates can be problematic. Understanding the different ways to overcome the limitations of CMR in arrhythmia is essential for providing high-quality imaging, comprehensive information, and definitive answers in this diverse group of patients.

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning

Aims

Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification.

Methods and results

We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models.

Conclusion

A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.

Cardiac Magnetic Resonance in Hypertensive Heart Disease: Time for a New Chapter

Hypertension is one of the most important cardiovascular risk factors, associated with significant morbidity and mortality. Chronic high blood pressure leads to various structural and functional changes in the myocardium. Different sophisticated imaging methods are developed to properly estimate the severity of the disease and to prevent possible complications. Cardiac magnetic resonance can provide a comprehensive assessment of patients with hypertensive heart disease, including accurate and reproducible measurement of left and right ventricle volumes and function, tissue characterization, and scar quantification. It is important in the proper evaluation of different left ventricle hypertrophy patterns to estimate the presence and severity of myocardial fibrosis, as well as to give more information about the benefits of different therapeutic modalities. Hypertensive heart disease often manifests as a subclinical condition, giving exceptional value to cardiac magnetic resonance as an imaging modality capable to detect subtle changes. In this article, we are giving a comprehensive review of all the possibilities of cardiac magnetic resonance in patients with hypertensive heart disease.

Hypertrophic Cardiomyopathy and Primary Restrictive Cardiomyopathy: Similarities, Differences and Phenocopies

Hypertrophic cardiomyopathy (HCM) and primary restrictive cardiomyopathy (RCM) have a similar genetic background as they are both caused mainly by variants in sarcomeric genes. These “sarcomeric cardiomyopathies” also share diastolic dysfunction as the prevalent pathophysiological mechanism. Starting from the observation that patients with HCM and primary RCM may coexist in the same family, a characteristic pathophysiological profile of HCM with restrictive physiology has been recently described and supports the hypothesis that familiar forms of primary RCM may represent a part of the phenotypic spectrum of HCM rather than a different genetic cardiomyopathy. To further complicate this scenario some infiltrative (amyloidosis) and storage diseases (Fabry disease and glycogen storage diseases) may show either a hypertrophic or restrictive phenotype according to left ventricular wall thickness and filling pattern. Establishing a correct etiological diagnosis among HCM, primary RCM, and hypertrophic or restrictive phenocopies is of paramount importance for cascade family screening and therapy.